Exploring wind data using the Meteostat
Group Members: Travis, Ira, Micah
Course: Data Science
Goal: Use ML models to predict wind trends in distinct U.S. regions
Source: Meteostat Python API
Dataset Type: Aggregated weather observations per station
Key Variableswspd: Average wind speed (mph)wdir: Mean wind direction (degrees)Time Period: 2024
Models: Wind Speed, Wind Direction, Locations
Frame: Hourly and Daily
Using Pittsburgh station
Missing Values
temp 0
dwpt 0
rhum 0
prcp 1091
snow 8761
wdir 0
wspd 0
wpgt 8761
pres 0
tsun 8761
coco 6
dtype: int64
Lagged 1, 3, and 6 hours before
TimeSeriesSplit with n_splits = 5
Linear Regression
MAE: 3.223
RMSE: 4.351
R²: 0.675
HistGradientBoostingRegressor
MAE: 2.649
RMSE: 3.906
R²: 0.743
Chicago, Denver, Miami, Phoenix, Seattle
Target VariableWind direction (degrees)
Training features*See visuals for further model comparisons
The features used to predict the weather are the wind speed and direction for the following weather stations:
the values we are trying to predict is the wind speed and direction for Greater Pittsburgh International Airport
the model got the folowing metrics on the test set.
see the last code cell to see a visualization of the predicted vs actual wind speed.
potential improvements:
R²: 0.620057846963885
RMSE: 54.64698786819519